This invention relates generally to the field of energy consumption metering, and more specifically to a cognitive electric power meter having embedded intelligence to decompose metered current and voltage signals into specific constituent energy consumption.
As the cost of energy/electricity continues to increase, consumers are becoming more conscious of their consumption and more thoughtful in terms of sustainable energy planning. People are buying more cars that get higher fuel mileage, for example, including both smaller and hybrid electric cars.
In order for people to use less energy/electricity in their homes, they need an itemized bill that clearly shows their usage and energy cost for each of their appliances. With itemized data, consumers can take action to conserve, by either installing more energy efficient appliances (air conditioners, cloths washers/dryers, hot tubs, ovens, lighting, etc), or changing their usage patterns in areas where pricing of energy/electricity varies by time of day, or simply turning loads off when not in use. The problem is that people do not want to incur the significant expense required to install power sensors on each of their appliances and electric loads.
One technique of decomposing the power signal measured at the incoming power meter into its constituent individual loads is known as Single Point End-use Energy Disaggregation (SPEED™), and is available from Enetics, Inc. of New York. The SPEED™ product includes logging premises load data and then transferring the data via telephone, walk-ups, or alternative communications to a Master Station that processes the recorder data into individual load interval data, acts as a server and database manager for pre and post processed energy consumption data, temperature data, queries from analysis stations, and queries from other information systems. This known technique runs on a Windows® operating system.
Although known decomposition techniques have succeeded in improving the quality of services related to consumer energy consumption, a need still exists for a more comprehensive electric power meter that does not require a Master Station and/or additional people resources to decompose an electric power meter signal into its constituent individual loads.
In view of the foregoing, it would be both beneficial and advantageous to provide an electric power meter that employs embedded intelligence to decompose the power signal that is already measured at the incoming meter into its constituent individual loads and to provide a usage summary to the consumer with no in home field installation cost.